On 6th July 2006, an intense swarm of earthquake activity began in the Sulu Range, Central New Britain, Papua New Guinea. The earthquakes were felt almost every one to two minutes, 24 hours a day, with modified Mercalli intensities of MM1 to MM4. They were accompanied by unusual vigorous activity in the hot springs southwest of the Sulu Range. Fearing a possible eruption and tsunami, about 1000 locals were evacuated.

The Philippine archipalego is tectonically complex and seismically hazardous, yet few seismic hazard assessments have provided national coverage. This paper presents an updated probabilistic seismic hazard analysis for the nation. Active shallow crustal seismicity is modeled by faults and gridded point sources accounting for spatially variable occurrence rates. Subduction interfaces are modelled with faults of complex geometry. Intraslab seismicity is modeled by ruptures filling the slab volume. Source geometries and earthquake rates are derived from seismicity catalogs, geophysical datasets, and historic-to-paleoseismic constraints on fault slip rates. The ground motion characterization includes models designed for global use, with partial constraint by residual analysis. Shallow crustal faulting near metropolitan Manila, Davao, and Cebu dominates shaking hazard. In a few places, peak ground acceleration with 10% probability of exceedance in 50 years on rock reaches 1.0 g. The results of this study may assist in calculating the design base shear in the National Structural Code of the Philippines.

The Philippine Institute of Volcanology and Seismology (PHIVOLCS) and Geoscience Australia (GA) have developed a long-term partnership in order to better understand and reduce the risks associated with earthquake hazards in the Philippines. The Project discussed herein was supported by the Australian Agency for International Development (AusAID). Specifically, this partnership was designed to enhance the exposure and damage estimation capabilities of the Rapid Earthquake Damage Assessment System (REDAS), which has been designed and built by PHIVOLCS. Prior to the commencement of this Project, REDAS had the capability to model a range of potential earthquake hazards including ground shaking, tsunami inundation, liquefaction and landslides, as well as providing information about elements at risk (e.g., schools, bridges, etc.) from the aforementioned hazards. The current Project enhances the exposure and vulnerability modules in REDAS and enable it to estimate building damage and fatalities resulting from scenario earthquakes, and to provide critical information to first-responders on the likely impacts of an earthquake in near real-time. To investigate this emergent capability within PHIVOLCS, we have chosen the pilot community of Iloilo City, Western Visayas. A large component of this project has been the compilation of datasets to develop building exposure models, and subsequently, developing methodologies to make these datasets useful for natural hazard impact assessments. Collection of the exposure data was undertaken at two levels: national and local. The national exposure dataset was gathered from the Philippines National Statistics Office (NSO) and comprises basic information on wall type, roof type, and floor area for residential buildings. The NSO census dataset also comprises crucial information on the population distribution throughout the Philippines. The local exposure dataset gathered from the Iloilo City Assessors Office includes slightly more detailed information on the building type for all buildings (residential, commercial, government, etc.) and appears to provide more accurate information on the floor area. However, the local Iloilo City dataset does not provide any information on the number of people that occupy these buildings. Consequently, in order for the local data to be useful for our purposes, we must merge the population data from the NSO with the local Assessors Office data. Subsequent validation if the Iloilo City exposure database has been conducted through targeted foot-based building inventory surveys and has allowed us to generate statistical models to approximate the distribution of engineering structural systems aggregated at a barangay level using simple wall and roof-type information from the NSO census data. We present a comparison of the national and local exposure data and discuss how information assembled from the Iloilo City pilot study - and future study areas where detailed exposure assessments are conducted - could be extended to describe the distribution of building stock in other regions of the Philippines using only the first-order national-scale NSO data. We present exposure information gathered for Iloilo City at barangay level in a format that can be readily imported to REDAS for estimating earthquake impact.

Many earthquakes in Indonesia have caused a large number of fatalities. Disaster risk-reduction of fatalities requires a representative fatality model derived from fatality data caused by historical earthquakes in Indonesia. We develop an empirical fatality model for Indonesia by relating macroseismic intensity to fatality rate using compiled subdistrict level fatality rate data and numerically simulated ground shaking intensity for four recent damaging events. The fatality rate data are compiled by collecting population and fatality statistics of the regions impacted by the selected events. The ground shaking intensity is numerically estimated by incorporating a finite fault model of each event and local site conditions approximated by topographically-based site amplifications. The macroseismic intensity distribution of each event is generated by using ShakeMap software with a selected pair of ground motion predictive equation (GMPE) and ground motion to intensity conversion equation (GMICE). The developed fatality model is a Bayesian generalized linear model where the fatality rate is assumed to follow a mixture of a Bernoulli and a gamma distribution. The probability of zero fatality rate and the mean non-zero fatality rate is linked to a linear function of shaking intensity by the logit and the log link functions, respectively. We estimate posterior distribution of the parameters of the model based on the Hamilton Monte Carlo algorithm. For validation of the developed model we calculate fatalities of the past events from the EXPO-CAT catalog and compare the estimates with the EXPO-CAT fatality records. While the developed fatality model can provide an estimate of the range of fatalities for future events it needs on-going refinement by incorporation of additional fatality rate data from past and future events.

Heterogeneous distribution of slip during megathrust earthquakes has been shown to significantly affect the spatial distribution of tsunami height in both numerical studies and field observations. This means that tsunami hazard maps generated using uniform slip distributions in their tsunami source models may underestimate tsunami inundation in some locations compared with real events of the same magnitude in the same location. In order to more completely define areas that may be inundated during a tsunami it is important to consider how different possible distributions of slip will impact different parts of the coastline. We generate tsunami inundation maps for the Mentawai Islands, West Sumatra, Indonesia, from a composite suite of possible source models that are consistent with current knowledge of the source region. First, a suite of earthquake source models with randomly distributed slip along the Mentawai Segment of the Sunda Subduction Zone is generated using a k-2 rupture model. From this suite we select source models that generate vertical deformation consistent with that observed in coral palaeogeodetic records of previous ruptures of the Mentawai Segment in 1797 and 1833, minus deformation observed in the 2007 Bengkulu earthquake sequence. Tsunami inundation is then modelled using high resolution elevation data for selected source models and the results compiled to generate a maximum tsunami inundation zone. This method allows us to constrain the slip distribution beneath the Mentawai Islands, where coral palaeogeodetic data is available, while allowing for greater variation in the slip distribution away from the islands, in particular near the trench where large slip events can generate very large tsunami. This method also allows us to consider high slip events on deeper portions of the megathrust between the Mentawai Islands and the Sumatran Mainland, which give greater tsunami inundation on the eastern part of the Mentawai Islands and the west coast of Sumatra compared with near-trench event. By accounting for uncertainty in slip distribution, the resulting hazard maps give a more complete picture of the areas that may be inundated compared with hazard maps derived from a single 'worst case' source model. These maps allow for more robust tsunami evacuation plans to be developed to support immediate community evacuation in response to strong or long-lasting earthquake ground shaking. From the American Geophysical Union Fall Meeting Abstracts

With a population of over 250 million people, Indonesia is the fourth most populous country in the world (United Nations, 2013). Indonesia also experiences more earthquakes than any other country in the world (USGS, 2015). Its borders encompass one of the most active tectonic regions on Earth including over 18 000 km of major tectonic plate boundary, more than twice that of Japan or Papua New Guinea (Bird, 2003). The potential for this tectonic activity to impact large populations has been tragically demonstrated by the 20004 Sumatra earthquake and tsunami. In order to inform earthquake risk reduction in Indonesia, a new national earthquake hazard map was developed in 2010 (Irsyam et al., 2010). In this report historical records of damaging earthquakes from the 17th to 19th centuries are used to test our current understanding of earthquake hazard in Indonesia and identify areas where further research is needed. In this report we address the following questions: - How well does our current understanding of earthquake hazard in Indonesia reflect historical activity? - Can we associate major historical earthquakes with known active faults, and are these accounted for in current assessments of earthquake hazard? - Does the current earthquake hazard map predict a frequency and intensity of shaking commensurate with the historical record? - What would the impact of these historical earthquakes be if they were to reoccur today? To help answer questions like these, this report collates historical observations of eight large earthquakes from Java, Bali and Nusa Tenggara between 1699 and 1867. These observations are then used to: - Identify plausible sources for each event; - Develop ground shaking models using the OpenQuake Engine (GEM Foundation, 2015); - Assess the validity of the current national seismic hazard map; and - Estimate fatalities were the historical events to occur today using the InaSAFE (InaSAFE.org, 2015) software.

On the 30th September 2009 a magnitude 7.6 earthquake struck West Sumatra in the Padang and Pariaman regions. It caused widespread damage to buildings and resulted and an estimated 1,117 fatalities. Thankfully the event was not accompanied by a tsunami that could have had additional devastating impacts and a greatly increased mortality. Under its mandate the AIFDR responded to the earthquake event with the objective of deriving an understanding of the factors that had contributed to outcome. It supported a team of Indonesian and international engineers and scientists who collected and analysed damage information that could subsequently be used for future disaster risk reduction in West Sumatra and Indonesia more broadly. The activity was jointly led by the Centre for Disaster Mitigation at the Institut Teknologi Bandung (ITB) and Geoscience Australia. This report provides a background to the region, describes the nature of the earthquake and its impacts, details the survey activity and outlines the significant outcomes that has come from it. Importantly, it makes several recommendations to assist in the regional reconstruction after the event and to guide future development in the Padang region and Indonesia more generally.

Tsunami hazard modelling for Tonga shows the potential impacts of tsunami generated by a very large earthquake on the nearby Tongan Trench.

Historical reports of earthquake effects from the period 1681 to 1877 in Java, Bali and Nusa Tenggara are used to independently test ground motion predictions in Indonesia’s 2010 national probabilistic seismic hazard assessment (PSHA). Assuming that strong ground motion occurrence follows a Poisson distribution, we cannot reject Indonesia’s current PSHA for key cities in Java at 95% confidence. However, the results do suggest that seismic hazard may be underestimated for the megacity Jakarta. Ground motion simulations for individual large damaging events are used to identify plausible source mechanisms, providing insights into the major sources of earthquake hazard in the region and possible maximum magnitudes for these sources. The results demonstrate that large intraslab earthquakes have been responsible for major earthquake disasters in Java, including a ~Mw 7.5 intraslab earthquake near Jakarta in 1699 and a ~Mw 7.8 event in 1867 in Central Java. The results also highlight the potential for large earthquakes to occur on the Flores Thrust. We require an earthquake with Mw 8.4 on the Flores Thrust to reproduce tsunami observation from Sulawesi and Sumbawa in 1820. Furthermore, large shallow earthquakes (Mw > 6) have occurred in regions where active faults have not been mapped identifying the need for further research to identify and characterize these faults for future seismic hazard assessments. <b>Citation:</b> Jonathan Griffin, Ngoc Nguyen, Phil Cummins, Athanasius Cipta; Historical Earthquakes of the Eastern Sunda Arc: Source Mechanisms and Intensity‐Based Testing of Indonesia’s National Seismic Hazard Assessment. <i>Bulletin of the Seismological Society of America </i>2018; 109 (1): 43–65. doi: https://doi.org/10.1785/0120180085

Papua New Guinea (PNG) lies in a belt of intense tectonic activity that experiences high levels of seismicity. Although this seismicity poses significant risks to society, the Building Code of PNG and its underpinning seismic loading requirements have not been revised since 1982. This study aims to partially address this gap by updating the seismic zoning map on which the earthquake loading component of the building code is based. We performed a new probabilistic seismic hazard assessment for PNG using the OpenQuake software developed by the Global Earthquake Model Foundation (Pagani et al. 2014). Among other enhancements, for the first time together with background sources, individual fault sources are implemented to represent active major and microplate boundaries in the region to better constrain the earthquake-rate and seismic-source models. The seismic-source model also models intraslab, Wadati–Benioff zone seismicity in a more realistic way using a continuous slab volume to constrain the finite ruptures of such events. The results suggest a high level of hazard in the coastal areas of the Huon Peninsula and the New Britain – Bougainville region, and a relatively low level of hazard in the southwestern part of mainland PNG. In comparison with the seismic zonation map in the current design standard, it can be noted that the spatial distribution of seismic hazard used for building design does not match the bedrock hazard distribution of this study. In particular, the high seismic hazard of the Huon Peninsula in the revised assessment is not captured in the current building code of PNG. <b>Citation:</b> Ghasemi, H., Cummins, P., Weatherill, G. <i>et al.</i> Seismotectonic model and probabilistic seismic hazard assessment for Papua New Guinea. <i>Bull Earthquake Eng, </i><b>18</b>, 6571–6605 (2020). https://doi.org/10.1007/s10518-020-00966-1